Eigenvalue Algorithms

Eigenvalue Algorithms is a class of numerical methods designed to compute eigenvalues and associated eigenvectors of a matrix by iteratively transforming or decomposing the matrix to reveal its spectral properties.

2.9K

Publications

193.6K

Citations

4.2K

Authors

1.4K

Institutions

Publications per year

2017–2026

276

Authors

4.2K

  1. GH
    Gene H. Golub

    Stanford University, United States

    27 publications · 6.5K citations

    21

    h-index

  2. JD
    James Demmel

    University of California, Berkeley, United States

    24 publications · 2.2K citations

    19

    h-index

  3. KM
    Karl Meerbergen

    KU Leuven, Belgium

    23 publications · 2.4K citations

    19

    h-index

  4. BΝ
    Beresford Ν. Parlett

    University of California, Berkeley, United States

    24 publications · 1.9K citations

    19

    h-index

  5. DS
    David S. Watkins

    Washington State University, United States

    27 publications · 2.2K citations

    18

    h-index

1–5 of 4.2K

Venues

  1. SIAM Journal on Matrix Analysis and Applications

    215 publications · 15.3K citations

    55

    h-index

  2. Linear Algebra and its Applications

    153 publications · 7.6K citations

    43

    h-index

  3. SIAM Journal on Scientific Computing

    101 publications · 6.6K citations

    42

    h-index

  4. Mathematics of Computation

    MCOM · 68 publications · 7.4K citations

    35

    h-index

  5. IEEE Transactions on Signal Processing

    51 publications · 5.3K citations

    33

    h-index

1–5